ELY NOVITA SARI, NPM 2109500151 (2025) KLASIFIKASI JENIS BUNGA IRIS BERDASARKAN FITUR MORFOLOGI MENGGUNAKAN ALGORITMA NAIVE BAYES. Tugas_Akhir(Artikel) Building of Informatics, Technology and Science (BITS), 7 (1). pp. 538-549. ISSN 2685-3310 (e-ISSN) 2684-8910 (p-ISSN)
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Abstract
Penelitian ini bertujuan untuk mengklasifikasikan jenis bunga Iris berdasarkan fitur morfologi menggunakan algoritma Naive Bayes. Bunga Iris terdiri dari tiga jenis, yaitu Iris-Setosa, Iris-Versicolor, dan Iris-Virginica, yang dapat dibedakan berdasarkan panjang dan lebar kelopak serta panjang dan lebar daun. Dataset yang digunakan dalam penelitian ini adalah dataset Iris yang berisi informasi mengenai empat fitur morfologi dari ketiga jenis bunga tersebut. Algoritma Naive Bayes dipilih karena memiliki keunggulan dalam melakukan klasifikasi berbasis probabilitas secara sederhana, cepat, dan efektif, terutama untuk data dengan fitur-fitur independen. Tahapan penelitian ini meliputi pengumpulan data, ekstraksi fitur, pembagian data menjadi data latih dan data uji, pelatihan model menggunakan algoritma Naive Bayes, serta pengujian model untuk mengevaluasi akurasi klasifikasi. Hasil penelitian menunjukkan bahwa model Naive Bayes mampu mengklasifikasikan data uji dengan baik, di mana nilai probabilitas tertinggi diperoleh pada kelas Iris-Versicolor, dengan nilai P(Versicolor│X)=1. Hal ini membuktikan bahwa data uji memiliki kemiripan paling tinggi terhadap spesies tersebut jika dibandingkan dengan dua spesies lainnya. Dengan demikian, algoritma Naive Bayes efektif diterapkan untuk klasifikasi jenis bunga Iris berdasarkan fitur morfologi yang dimiliki.. Kata Kunci: Klasifikasi, Bunga Iris, Fitur Morfologi, Naive Bayes, Data Mining ========================================================== This study aims to classify the types of Iris flowers based on morphological features using the Naive Bayes algorithm. Iris flowers consist of three types, namely Iris-Setosa, Iris-Versicolor, and Iris-Virginica, which can be distinguished based on the length and width of the petals as well as the length and width of the sepals. The dataset used in this research is the Iris dataset, which contains information on four morphological features from these three types of flowers. The Naive Bayes algorithm was chosen because of its advantages in performing probability-based classification in a simple, fast, and effective manner, especially for data with independent features. The research stages include data collection, feature extraction, splitting the data into training and testing sets, training the model using the Naive Bayes algorithm, and testing the model to evaluate classification accuracy. The results of the study show that the Naive Bayes model is able to classify the test data accurately, with the highest probability value obtained in the Iris-Versicolor class, with a value of P(Versicolor│X)=1. This indicates that the test data has the highest similarity to that species compared to the other two species. Thus, the Naive Bayes algorithm proves effective for classifying types of Iris flowers based on their morphological features. Keywords: Classification, Iris Flowers, Morphological Features, Naive Bayes, Data Mining
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Klasifikasi, Bunga Iris, Fitur Morfologi, Naive Bayes, Data Mining================Classification, Iris Flowers, Morphological Features, Naive Bayes, Data Mining |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 22 Oct 2025 09:38 |
| Last Modified: | 22 Oct 2025 09:38 |
| URI: | http://repository.ulb.ac.id/id/eprint/1842 |
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